data: $integral dS$ [ Hydromet Seasonal_Forecast Precipitation target

served from 181.199.253.14

∫dS [ Hydromet Seasonal_Forecast Precipitation target_date ]: forecast time data

Precipitation target_date int_dS forecast time from Hydromet Seasonal_Forecast: Seasonal Forecast.

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered [ (2.5)] :grid
S (forecast_reference_time): grid: /S (days since 1960-01-01) ordered [ (0000 16 Mar 2019) (0000 16 Apr 2019) (0000 16 May 2019) (0000 16 Jun 2019) (0000 16 Jul 2019) (0000 16 Aug 2019) (0000 16 Sep 2019) (0000 16 Oct 2019) (0000 16 Nov 2019) (0000 16 Dec 2019) (0000 16 Jan 2020) (0000 16 Feb 2020) (0000 16 Mar 2020) (0000 16 Apr 2020) (0000 16 May 2020) (0000 16 Jun 2020) (0000 16 Jul 2020) (0000 16 Aug 2020) (0000 16 Sep 2020) (0000 16 Oct 2020) (0000 16 Nov 2020) (0000 16 Dec 2020) (0000 16 Jan 2021) (0000 16 Feb 2021) (0000 16 Mar 2021) (0000 16 Apr 2021) (0000 16 May 2021) (0000 16 Jun 2021) (0000 16 Jul 2021) (0000 16 Aug 2021)] :grid

Other Info

bufferwordsize: 8
calendar: 360
CE: null
CS: null
datatype: doublearraytype
gridid: 184180080
pointwidth: 3
scale_max: null
scale_min: null
standard_name: forecast_reference_time
units: 0.00694444444444444 ( year above 2000 ) year

Last updated: Fri, 12 Jan 2024 14:16:35 GMT
Expires: Fri, 09 Feb 2024 00:00:00 GMT

Data Views

	L	S
Hydromet Seasonal_Forecast Precipitation[target_length hindcast_date History target_date Forecast hindcast_length ]
$integral dS$ [ Hydromet Seasonal_Forecast Precipitation target_date ] 2.5 months[ S L \|]	M	M

Filters

Here are some filters that are useful for manipulating data. There are actually many more available, but they have to be entered manually. See Ingrid Function Documentation for more information.

Monthly Climatology calculates a monthly climatology by averaging over all years.
anomalies calculates the difference between the (above) monthly climatology and the original data.
Integrate along S
Differentiate along S
Take differences along S